Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	60,388
2	South Dakota	54,088
3	Wisconsin	42,074
4	Iowa	42,058
5	Mississippi	40,611
6	Louisiana	40,439
7	Alabama	39,748
8	Tennessee	38,112
9	Florida	37,809
10	Arkansas	37,656
11	Nebraska	37,546
12	Idaho	37,071
13	Utah	36,714
14	Georgia	35,532
15	South Carolina	34,749
16	Arizona	34,112
17	Illinois	34,034
18	Texas	33,281
19	Nevada	33,194
20	Rhode Island	32,208
21	Montana	32,108
22	Missouri	31,916
23	Oklahoma	31,639
24	Kansas	31,103
25	Indiana	27,875
26	New Jersey	27,334
27	Minnesota	27,248
28	North Carolina	26,551
29	New York	26,530
30	Delaware	25,992
31	Kentucky	25,027
32	District of Columbia	24,708
33	Wyoming	24,478
34	Maryland	24,393
35	California	23,894
36	Massachusetts	23,293
37	Alaska	23,167
38	New Mexico	22,941
39	Virginia	21,488
40	Puerto Rico	21,345
41	Connecticut	20,715
42	Michigan	20,440
43	Colorado	19,591
44	Ohio	18,984
45	Pennsylvania	16,969
46	Washington	15,074
47	West Virginia	14,080
48	Oregon	10,901
49	Hawaii	10,847
50	New Hampshire	8,325
51	Maine	5,057
52	Vermont	3,556

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,544
2	South Dakota	1,240
3	Wisconsin	737
4	Montana	724
5	Iowa	710
6	Wyoming	656
7	Illinois	556
8	Alaska	521
9	Nebraska	514
10	Utah	496
11	Minnesota	482
12	Indiana	460
13	Colorado	456
14	Kansas	439
15	Missouri	438
16	Rhode Island	392
17	Idaho	386
18	Michigan	359
19	New Mexico	348
20	Kentucky	313
21	Oklahoma	311
22	Arkansas	305
23	Alabama	298
24	Ohio	260
25	Puerto Rico	253
26	Connecticut	247
27	Tennessee	241
28	Nevada	238
29	West Virginia	231
30	Texas	227
31	Georgia	223
32	South Carolina	215
33	North Carolina	198
34	Arizona	192
35	Delaware	191
36	Florida	183
37	Pennsylvania	173
38	Mississippi	171
39	New Jersey	167
40	Massachusetts	164
41	Maryland	147
42	Virginia	147
43	District of Columbia	138
44	Washington	126
45	Oregon	125
46	New Hampshire	106
47	California	103
48	New York	101
49	Louisiana	96
50	Maine	56
51	Hawaii	50
52	Vermont	34

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,841
2	New York	1,705
3	Massachusetts	1,454
4	Connecticut	1,297
5	Louisiana	1,276
6	Rhode Island	1,142
7	Mississippi	1,124
8	District of Columbia	916
9	Arizona	821
10	Illinois	798
11	Florida	783
12	Michigan	772
13	South Carolina	766
14	Georgia	735
15	Delaware	729
16	North Dakota	715
17	Pennsylvania	694
18	Maryland	687
19	Arkansas	657
20	Indiana	652
21	Texas	642
22	Alabama	606
23	Nevada	579
24	Iowa	556
25	Missouri	505
26	South Dakota	495
27	New Mexico	494
28	Tennessee	490
29	Ohio	456
30	Minnesota	450
31	California	447
32	Virginia	428
33	North Carolina	420
34	Colorado	402
35	Wisconsin	363
36	Montana	362
37	Kansas	359
38	Idaho	358
39	New Hampshire	355
40	Kentucky	350
41	Oklahoma	342
42	Nebraska	340
43	Washington	326
44	Puerto Rico	262
45	West Virginia	255
46	Utah	191
47	Oregon	164
48	Hawaii	153
49	Wyoming	150
50	Maine	110
51	Alaska	107
52	Vermont	92

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	12
2	Arkansas	9
3	South Dakota	8
4	Indiana	5
5	Montana	5
6	Iowa	4
7	Missouri	4
8	New Mexico	4
9	Wisconsin	4
10	South Carolina	3
11	Alabama	2
12	Arizona	2
13	Delaware	2
14	Idaho	2
15	Illinois	2
16	Massachusetts	2
17	Minnesota	2
18	Mississippi	2
19	Nebraska	2
20	Oklahoma	2
21	Rhode Island	2
22	Alaska	1
23	Colorado	1
24	Connecticut	1
25	Florida	1
26	Georgia	1
27	Kansas	1
28	Kentucky	1
29	Louisiana	1
30	Michigan	1
31	North Carolina	1
32	Ohio	1
33	Oregon	1
34	Puerto Rico	1
35	Tennessee	1
36	Texas	1
37	Utah	1
38	West Virginia	1
39	California	0
40	District of Columbia	0
41	Hawaii	0
42	Maine	0
43	Maryland	0
44	Nevada	0
45	New Hampshire	0
46	New Jersey	0
47	New York	0
48	Pennsylvania	0
49	Vermont	0
50	Virginia	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	179,515	1	99
Chattahoochee	Georgia	167,599	2	99
Trousdale	Tennessee	159,695	3	99
Lafayette	Florida	151,864	4	99
Bon Homme	South Dakota	148,094	5	99
Davidson	Tennessee	50,569	327	89
Richland	South Carolina	42,611	538	82
York	South Carolina	25,077	1608	48
Orange	California	19,606	2065	34
Pierce	Washington	13,244	2544	19

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Jerauld	South Dakota	6,458	1	99
Hancock	Georgia	5,321	2	99
Emporia city	Virginia	5,238	3	99
Kenedy	Texas	4,950	4	99
Galax city	Virginia	4,727	5	99
Richland	South Carolina	661	894	71
Davidson	Tennessee	509	1213	61
Orange	California	467	1324	57
York	South Carolina	381	1573	49
Pierce	Washington	286	1859	40

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.